Texture classification using combination of LBP and GLRLM features along with KNN and multiclass SVM classification

Abstract

The paper presents a unique combination of texture feature extraction techniques which can be used in image texture analysis. Setting the prime objective of classifying different texture images, the Local Binary Pattern (LBP) and a modified form of Gray Level Run Length Matrix (GLRLM) are implemented initially. The next phase involves use of combination of the former two methods to extract improved features. The feature vectors were obtained by defining the features on the transformed images. These texture features are classified using two classification algorithms, KNN and multiclass SVM. The results of above feature extraction techniques with individual classifiers have been compared. The comparison yields that the combination of LBP and GLRLM texture features shows better classification rate than the features obtained from individual feature extraction techniques. Among the classifiers, Support Vector Machine has better classification rate than the Nearest Neighbor approach for the texture classification.